Spectroscopy instrument using broadband modulation and statistical estimation techniques to account for component artifacts

ABSTRACT

A spectroscopy instrument that uses spectra produced from random binary sequence modulated data. Statistical estimation techniques are used to achieve resolution enhancement, while properly accounting for the Poisson noise distribution and other artifacts introduced by a modulator or “chopper” or other system components. A resolution similar to that of modern spectrometers can be achieved. Both static and dynamic behaviors are theoretically or measured experimentally accounted for in the model as determined. In one embodiment, the finite penetration of the field beyond the plane of the chopper leads to non-ideal chopper response, which is characterized in terms of an “energy corruption” effect and a lead or lag in the time at which the beam responds to the chopper potential.

RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.10/924,282 U.S. Pat. No. 7,031,877, filed on Aug. 23, 2004, which is acontinuation of U.S. application Ser. No. 10/165,852 U.S. Pat. No.6,782,342, filed on Jun. 7, 2002, which claims the benefit of an earlierfiled U.S. Provisional Patent Application No. 60/296,850, filed on Jun.8, 2001, entitled “Method for Enhancement of Electron SpectrometerOperation Using Maximum Likelihood Spectral Estimation Techniques”, theentire contents of which are hereby incorporated by reference.

BACKGROUND OF THE INVENTION

Despite dramatic advances in the energy resolution and throughput ofelectron monochromators for high resolution electron energy lossspectroscopy (HREELS), a major limitation of conventional, dispersivesector, electron energy analyzers is that they are inherently serialdevices, leading to long data acquisition times. The advantage of higherresolution leads to trade-offs in performance (throughput) becausechannel step size must be reduced, and therefore increasing the numberof channels required to measure a given spectral region. Using amulti-channel plate detector to ameliorate this problem is onepossibility. Indeed, time-resolved HREELS measurements have beendemonstrated with a multi-channel plate in the dispersive plane of aconventional analyzer. However, parallel detection can be accomplishedin this way only over a limited spectral range without degradingresolution. Thus, development of an analyzer based upon paralleldetection would benefit both typical spectral investigations and allownew experiments to be performed, such as recent inelastic diffractionexperiments which are both momentum and energy resolved.

Pseudo-random binary sequences (PRBS), also known as maximal length (ML)shift register sequences and/or pseudorandom noise (PN) sequences, havebeen used for modulation of photon and particle beams in widely usedtime-of-flight (TOF) techniques. These find application in, for example,neutron scattering, molecular beam scattering, and ion massspectroscopy. The PRBS-TOF method achieves a throughput advantage oversingle pulse TOF due to the 50% duty cycle. PRBS modulation has beencombined with TOF-MS, for example in a paper by Brown, W. L., et al.,“Electronic sputtering of low temperature molecular solids,” in NuclearInstruments and Methods in Physics Research, Vol. B1, 1984, pp. 307-314.In this example, an incident ion beam was pulsed with a pseudo-randomsequence. The ion beam impinged upon a condensed water matrix sample,sputtering or producing secondary ions and neutrals which are measuredin a time-of-flight detector. An electron impact ionizer was used toionize the neutral and then a quadrupole filter was employed to massselect the products. Thus, in this case, the TOF technique was used tomeasure the energy distribution. To improve the signal-to-backgroundratio, PRBS modulation and cross-correlation recovery techniques wereused, assuming that the source modulation was ideal.

More specifically, in such approaches, the underlying TOF spectrum (theobject spectrum, o) is modulated with the PRBS sequence, p, resulting ina periodic, time sequence that is assumed to be defined mathematicallyas, (p{circle around (x)}o). In the standard cross-correlation recoverymethod, an estimate of the TOF spectrum, r, is obtained by correlatingthe detected TOF signal data with the PRBS modulation sequence, p:r=p⊕(p{circle around (x)}o). Here, {circle around (x)} and ⊕ denoteconvolution and correlation, respectively.

A special property of maximal length PRBS sequences is that theautocorrelation of the discrete binary sequence p is a substantially adelta function; therefore, the recovered spectrum, r, is substantiallyidentical to the original object spectrum, o. In reality, the modulationfunction p is continuous, but r is an estimate of o as long as the timebase (minimum pulse width) of the modulation function is small comparedto the linewidth of the narrowest features in o. If this is not thecase, then the throughput advantage is gained at the expense ofresolution in the recovered spectrum, and over-sampling of the modulatedsignal, (p{circle around (x)}o), leads to a recovered spectrum which isthe autocorrelation (p⊕p) (roughly, a triangular pulse) convoluted withthe object function: r=(p⊕p){circle around (x)}o .

In fact, the modulation of the particle beam, whether performed at thesource, with a spinning disk type of mechanical chopper, or with anelectrostatic deflection based device, is at best describedapproximately as a convolution with the ideal sequence, (p{circle around(x)}o). First, the actual effect of the modulating device on theparticle beam differs to some extent from the ideal sequence, p. Anumber of artifacts in the recovered object function, r, are well knownin the art, and some types of non-ideal behavior can be correctedthrough post processing when (p⊕p) differs from a delta function, suchas arises from machining errors in creating the slots in mechanicspinning disks. Second, most modulators do not act in exactly the samemanner on different particles in the beam; for example, the finitethickness of spinning disks leads to a velocity dependent modulationfunction in molecular beam scattering applications. In this case, theassumption of a convolution is not strictly true.

To the extent that the modulation can be described by a convolution, andthe actual modulation function, p, is known or can be estimated, theobject function may be recovered simply by Fourier deconvolution. Inpractice, the presence of noise in measured data complicatesdeconvolution of spectral data in the simplest cases when the instrumentfunction can be described by a single feature.

The deconvolution of a PRBS modulation sequence, in which the datacontains multiple overlapping copies of the underlying object function,has not been reported in spectroscopic applications, to our knowledge.

Probability-based estimation methods for recovery of one-dimensionaldistributions, and for resolution enhancement of one-dimensionalspectral data and two-dimensional image data, have been used byastronomers since 1972. (See Richardson, W. H. 1972, “Bayesian-BasedIterative Method of Image Restoration”, J. Opt. Soc. Am. 62, 55-59;Frieden, B. R. 1972, “Restoring with Maximum Entropy and MaximumLikelihood”, J. Opt. Soc. Am. 62, 511-18; Lucy, L. B. 1974, “Aniterative technique for the rectification of observed distributions”,Astron. J. 19, 745-754; and Ables, J. G. 1974, “Maximum Entropy SpectralAnalysis”, Astron. Ap. Suppl. 15, 383-93.) Recent success with iterativemaximum likelihood and Bayesian methods has been demonstrated in a paperby Frederick, B. G., et al., entitled “Spectral restoration in HREELS,”in the Journal of Electron Spectroscopy and Related Phenomena, Vol.64/65, 1993, pp. 825. The maximum likelihood result is simply an array,which convoluted with the modulation function, fits the data as well aspossible, given the noise distribution. A well known example of thisapproach is the algorithm reference in the paper by L. B. Lucy. TheBayesian method employed by Frederick, et al., includes a maximumentropy constraint that limits the degree of resolution enhancement in amanner that leads to a single converged estimate with no arbitraryadjustable parameters.

SUMMARY OF THE INVENTION

We have invented an instrument that uses a PRBS particle beam modulatorand detector together with a probability based estimation algorithm forremoving artifacts introduced by components of the instrument.

Specifically, in a preferred embodiment, an interleaved comb-typechopper can modulate an electron beam with rise and fall times of lessthan a nanosecond, which corresponds to meV energy resolution for lowenergy electrons. The finite penetration of the fields associated withthis electrostatic device produces certain non-ideal behavior, which wecharacterize in terms of an “energy corruption” effect and a lead or lagin the time at which the beam responds to the chopper potential.

According to our invention, for the first time, an instrument employsmaximum likelihood, maximum entropy, or other probability basedestimation methods to recover the underlying TOF spectrum, in spite ofthe corruption. These methods can be used to undo the corrupting effectsof (a) less than perfectly “maximal length” PRBS sequence; (b) specificchopper effects; and (c) in general, detected signal artifactsintroduced by components of the instrument.

Compared to the standard cross correlation method, i) the resolution isimproved relative to the nominal time base resolution of the PRBS orother modulating sequence; ii) the Poisson (pulse counting) noise isaccounted for; and iii) artifacts associated with imperfections of thechopper or other component performance are reduced.

A spectroscopy instrument thus makes use of statistical estimationtechniques to account for component artifacts in accordance with thepresent invention. The instrument may use several different types ofphysical phenomena to determine the attributes of a sample. In onespecific embodiment, a particle source such as ion source provides astream of particles to a propagation path. The instrument uses amodulator grid or “chopper”, driver electronics, and a sequencegenerator to modulate the ion source. The ion source may be modulateddirectly either prior to or subsequent to its application to a sample inorder to provide a particle beam that is modulated in time.

The modulator may itself take different forms; one particularly usefulimplementation as a grid of wires. In addition, spinning disk-typemodulators can be utilized which encode the specific modulation sequenceas a series of holes around the periphery.

Particles of different chemical makeup exhibiting different physicaltime-of-flight properties thus travel down the propagation path atdifferent times over different distances to arrive at one or moredetectors. A time-to-digital converter then provides a signal to acomputer to analyze the detected signal to determine the chemical makeupof the sample.

As has been alluded to above, the computer uses a component model thatmakes use of maximum likelihood estimation. In an implementation of astatistical method that uses a maximum likelihood method, the so-calledLucy algorithm can be used to refine the estimate for the objectspectrum. It will be understood by those of skill in the art that otheralgorithms can be used.

The computer may perform this statistical method as follows. Forexample, a system response function is first chosen. The system responsefunction may be an a priori measured response, such as the modulatedsignal measured with a monochromatic source or from a monochromator. Itmay also be obtained from a theoretical model, or from a set of datameasured on the same sample, for example a high resolution single pulseTOF spectrum and a PRBS modulated spectrum. If the single pulse spectrumis a good estimate of the underlying object spectrum, o, then the PRBSmodulated data, y, and then an estimation method such as Lucy can beused to obtain p by deconvolution of y with the estimated objectspectrum, o.

The computer also obtains an initial estimate, o_(i), of the objectspectrum. This can be from a previous spectrum or from performing across-correlation of the modulating sequence with system response data.

The system response and initial object spectrum estimate are thencombined with a model of the instrument that may for example include thenoise and physical characteristics of the instrument, to select anappropriate probability based estimation algorithm. A refined estimateis then obtained; the estimate obtained may be acceptable as determinedby criteria, or iteration via the refinement process may be necessary.

Thus, although PRBS modulation has been known and used in many areas ofspectroscopy in the prior art { including neutron scattering, molecularbeam scattering, TOF-MS and secondary ion mass spectroscopy (SIMS)}, andalthough digital signal processing methods for data recovery have beenutilized in an even wider range of spectroscopies, we know of noexamples in which the actual response function of the system,particularly the modulator, has been estimated and used to directlydeconvolute the PRBS modulated data. There are a number of reasons thatdeconvolution of PRBS modulated data may not have been contemplated inthis field. This may have been driven by the fact that thedelta-function autocorrelation properties of PRBS sequences werepresumed to provide perfect recovery of the underlying spectrum, andthat no further artifacts were introduced by the process.

There has in fact been a general skepticism towards deconvolution of onedimensional spectroscopic data, in part due to the difficulty of theinversion problem. The presence of noise in real data leads toartifacts, even when the response function is known accurately.Filtering usually involves adjustment of some arbitrary parameters, suchthat the estimate obtained is not unique and the results are subjective.Many methods require that assumptions be made about the underlyingspectrum, such as the shape and number of peaks. Furthermore, in thecase of PRBS modulation, in which the data contains multiple,overlapping copies of the desired object spectrum, it is not obvious, apriori, that

-   i. there is sufficient phase information to allow deconvolution of    the data, even for the case in which the response function is known    and the data is measured without noise; or-   ii. that existing algorithms may not converge to the true solution.

There has been an emphasis upon real-time display during dataacquisition, such that Fourier transform based instruments did notbecome popular until Fast Fourier transform algorithms and sufficientlyfast computers became available. The iterative methods we have utilizedhere have required sufficient computational power that real-timeprocessing during data acquisition has been a limitation; nevertheless,dramatic increases in computational power associated with DSP's andFPGA's now allow much more sophisticated processing in real-time.

Unlike traditional non-linear least squares fitting algorithms thatoptimize typically not more than 15 or 20 parameters, the methods wehave chosen require optimization of at least as many parameters as thereare points in the modulated time series data. The method makes noassumptions regarding the number or shape of features in the underlyingTOF spectrum, except that the spectrum is positive definite. Therefore,the inversion problem appears to be much more difficult than traditionnon-linear fitting problems.

A critical factor in our approach is to oversample the data, relative tothe PRBS time unit, which is counter to the prevailing practice in thefield. Brock et al, in U.S. Pat. No. 6,300,626 note that “This procedurewill increase the definition of individual peaks, but is not able toincrease the time or mass resolution of the device.” While this is truefor the measured time resolution, particularly for a single pulse TOFspectrum, the information content in the signal may be significantlyenhanced by oversampling the data and the system response function. Inaddition to dramatically reducing artifacts in the recovered spectrumdue to certain kinds of non-ideal behavior of the modulator, we havedemonstrated that a resolution enhancement by a factor of at least 8×can be achieved with PRBS modulation. This is in part due to the squarepulse like shape of the response function, retaining relatively highfrequency components in the Fourier domain.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other objects, features and advantages of theinvention will be apparent from the following more particulardescription of preferred embodiments of the invention, as illustrated inthe accompanying drawings in which like reference characters refer tothe same parts throughout the different views. The drawings are notnecessarily to scale, emphasis instead being placed upon illustratingthe principles of the invention.

FIG. 1 is a block diagram of components of an instrument constructedaccording to the invention.

FIG. 2 is a flow diagram of the process steps performed by theinstrument.

FIGS. 3 a-3 f are illustrations of the non-ideal chopper response on theautocorrelation function. Specifically, FIG. 3 a is a segment of anideal 2⁸-1 bit PRBS sequence which is over sampled by a factor of 16 andFIG. 3 b its autocorrelation function; FIG. 3 c is the effect of alinear rise time on FIG. 3 d, the autocorrelation function; and FIG. 3 eis the effect of exponential rise and fall times with a duty cycle lessthan 50% producing artifacts in FIG. 3 f, the autocorrelation function.The central section of each autocorrelation function is expanded to showthe peak shape.

FIGS. 4 a and 4 b quantify the effect of the finite field penetration onthe energy of a charged particle for a chopper known as aBradbury-Neilsen gate, as a function of the position of particle afterthe gate at the time the deflection voltages are applied. Theinformation is given in terms of energy corruption histograms for theexample of a 2 eV electron. Histograms obtained via a Monte-Carlosampling of the model potential for 2 eV electrons arriving at thechopper within the time intervals in curve (a) of 0-0.5 ns, curve (b) of0.5-1 ns curve (c) of 1-1.5 ns, curve (d) of 1.5-2 ns, curve (e) of2-2.5 ns, curve (f) of 2.5-3 ns, and curve (g) of 3-3.5 ns.

FIG. 5 is a comparison of the object spectrum with simulated singlepulse TOF spectra (gate open 8 ns) and the spectra recovered from a PRBSmodulated experiment using the cross correlation method. Acquisitiontimes of 1 and 256 sec are shown for an incident beam current of 10⁶counts/sec. Here, the response function is the ideal (step function)PRBS sequence as in FIG. 3.

FIG. 6 is a comparison of the object function with estimates recoveredfrom PRBS modulated data using the cross correlation method and themaximum likelihood method (for 50, 500 and 5000 iterations). Themodulation function contains linear rise times as in FIG. 3. Data shownfor 256×10⁶ counts total in the modulated data.

FIG. 7 a is a comparison of the object function with estimates recoveredfrom PRBS modulated data that includes the non-ideal chopper responseusing the maximum likelihood method (for 50, 500 and 5000 iterations).The modulation function contains both energy corruption and the time lagin opening and closing based upon our model of the interleaved combchopper and the energy distribution of the monochromatic beam. Datashown for 256×10⁶ counts total in the modulated data.

FIG. 7 b is an illustration of the negative-going artifacts in the crosscorrelation recovery due to the non-ideal response of the chopper(middle curve) and the importance of accurately determining the responsefunction: results (upper curve) of using the ideal (step function) PRBS(FIG. 3) sequence in the maximum likelihood method.

FIGS. 8 a and 8 b are schematic diagrams of systems designed tocharacterize choppers. The chopper is mounted on the monochromator atthe center of rotation and rotates with it to measure. FIG. 8 a showsenergy and angular distributions ontained with the conventional analyzeras a function of the applied static potential, ±V_(app); and FIG. 8 btime-dependent response and angular distributions with a TOF detector.

FIG. 9 a shows a model for an inifinte array of infinitely long wires ofradius, R, and spacing, d, in the x=0 plane with alternating potential±V_(app).

FIG. 9 b shows the resulting two-wire problem obtained by conformalmapping.

FIG. 10 a shows a typical angular distribution, measured with the TOFdetector, for a 5 eV beam as a function of the static DC voltage,V_(app), applied to the chopper.

FIG. 10 b shows theoretical angular distributions for a uniformlydistributed (see text) beam of electrons (E_(p)=5 eV) as a function ofapplied voltage, V_(app), from trajectory calculations. Inset comparesthe experimental and theoretical peak deflection angle as a function theratio V_(app)/KE. Wire radius, R=25 μm; wire spacing, d=1.2 mm; flightdistance 160 mm; acceptance angle ±1°; angular bin size=0.1°.

FIGS. 11 a-d show the measured time-dependent response of a chopper,with wire diameter, 2R=100 μm, and spacing, d=1 mm, to a 5 eV beam as afunction of the chopper potential, V_(app). FIG. 11 a for 0.25 V; FIG.11 b for 0.5 V; FIG. 11 c for 1.0 V; and FIG. 11 d for 2.0v.

FIG. 11 e illustrates the difference between the time that the beam ison the time that the chopper voltages are off.

FIGS. 12 a-c show calculated time-dependent responses as a function ofapplied voltage, V_(app); FIG. 12 a 0.5 V, FIG. 12 b 1.0 V and FIG. 12 c2.0 V.

FIGS. 12 d-f show this response as a function of wire spacing atconstant R/d (i.e. constant transmission) for V_(app)=1.0 V. The peaksand tails, due to energy corruption, as well as lag and lead effects,decrease with V_(app) and with d.

FIG. 13 shows a contour map of the potential, ψ(x,y), with criticalboundaries, ƒ(x,y, θ_(acc), V_(app)), as a function of the acceptanceangle and applied voltage. If the electron is at position (x₀, y₀)beyond the boundary at the time when the potential is applied, theelectron has final angle θ_(ƒ)<θ_(acc); i.e. the electron reaches thedetector, and suffers an energy corruption eψ(x₀,y₀).

FIG. 14 shows a schematic arrangement of a time-of-flight massspectrometer according to the present invention.

FIGS. 15 a-15 d illustrate schematically the process steps of a model ofthe processes occurring in the TOF mass spectrometer of FIG. 14 that canbe used in estimating the mass spectrum according to the methods of thepresent invention.

FIGS. 16 a and 16 b compare the results of using the Lucy algorithm torecover a TOF HREELS-like spectrum when maximal length (ML) and non-MLsequence of varying duty cycle are used for modulation.

FIGS. 17 a-17 c compare the true object spectrum (FIG. 17 a) with theresults of recovering synthetic data with the Lucy algorithm after 1000iterations when the data was modulated with a randomly chosen set of“1”s and “0”'s with a duty cycle of 25% in FIG. 17 b and 50% in FIG. 17c.

DETAILED DESCRIPTION OF A PREFERRED EMBODIMENT 1. Using StatisticalEstimation Techniques to Account for Component Artifacts in aSpectroscopy Instrument

FIG. 1 is a block diagram of a spectroscopy instrument that makes use ofstatistical estimation techniques to account for component artifacts inaccordance with the present invention. The instrument may use severaldifferent types of physical phenomena to determine the attributes of asample. In general, a particle source such as ion source 100 provides astream of particles to a propagation path 120. The spectrometer is atype of spectrometer that makes use of a modulator grid or “chopper”102, driver electronics 104, and sequence generator 106 to modulate theion source 100. Alternatively, the ion source may be modulated directlyeither prior to or subsequent to its application to a sample in order toprovide a particle beam that is modulated in time.

The modulator 102 may itself take different forms; one particularlyuseful implementation as a grid of wires is discussed in detail below.In the case of a grid modulator 102, the modulation sequence applied tothe modulator 102 may be generated by a shift register, stored in alook-up table or the memory of a digital signal processor or fieldprogrammable gate array, etc. In addition, spinning disk-type modulatorscan be utilized which encode the specific modulation sequence as aseries of holes around the periphery.

Particles of different chemical makeup exhibiting different physicaltime-of-flight properties thus travel down the propagation path atdifferent times over different distances to arrive at one or moredetectors 130. A time-to-digital converter 140 then provides a signal toa computer 150 to analyze the detected signal to determine the chemicalmakeup of the sample.

In accordance with one embodiment of the present invention, a componentmodel 160 that makes use of maximum likelihood estimation or otherstatistical estimation methods is used to process the detected signal.Shown in FIG. 1 is an implementation of a statistical method that uses amaximum likelihood method, the so-called Lucy algorithm (described indetail below) to refine the estimate for the object spectrum. It will beunderstood by those of skill in the art that other algorithms can beused.

The computer performs a number of component elements of the instrumentas follows. For example, at 152, a system response function is chosen.The system response function may be an a priori measured response, suchas the modulated signal measured with a monochromatic source or from amonochromator 153. It may also be obtained from a theoretical model 154.It may also be obtained from a set of data 155 measured on the samesample, for example a high resolution single pulse TOF spectrum and aPRBS modulated spectrum. If the single pulse spectrum is a good estimateof the underlying object spectrum, o, then the PRBS modulated data, y,and then an estimation method such as Lucy can be used to obtain p bydeconvolution of y with the estimated object spectrum, o.

At 155 the computer obtain an initial estimate, o_(i), of the objectspectrum. This can be obtained from a previous spectrum 157 or fromperforming a cross-correlation 158 of the modulating sequence withsystem response data.

The system response and initial object spectrum estimate are thencombined with a model 160 of the instrument that may for example includethe noise and physical characteristics of the instrument, to select anappropriate probability based estimation algorithm 162. A refinedestimate is then obtained at 164. The estimate obtained may beacceptable as determined by criteria 166, or iteration via therefinement process 164 may be necessary.

This embodiment of the instrument can also be thought of as performing asequence of processes as shown in FIG. 2. These include a first step ofproducing a modulated beam 205. The modulated particle beam is thenallowed to drift through a propagation path in step 210. In step 220, adetected signal is determined based upon particles arriving at the exitpoint of the propagation path. In a step 230, a maximum likelihoodalgorithm or other statistical estimation method is applied to thecollected signal in order to extract desired information about theoriginal particle beam. This, in turn, allows recovery of the spectrumin step 240.

The model used in the statistical estimation method can account forartifacts introduced by components of the instrument. More specifically,the non-ideal characteristics of the components of the system caninclude noise in the spectrum due to the nature of pulse countingelectronics (Poisson) or in the detector (Gaussian), lead/lag effects inthe chopper (modulator), energy spread in the ion beam, second ordereffects in a linear reflectron that do not completely compensate theenergy spread, residual electric and magnetic fields in the flight tube,and the like. We discuss in detail below a model of the effectsintroduced by a wire grid modulator.

We have found that implementing an instrument with maximum likelihood orother statistical based analysis can be an advantage to a range ofdifferent instruments, modulation techniques, propagation patharrangements, and the like.

For example, the specific sequence generated by sequence generator 106and applied to the modulator grid 102 may be a pseudo-random bitsequence (PRBS) of a so-called maximal length-type, but may also be moregenerally any sort of sequence that has a relatively broadband spectrumcontent. By this, we mean that given a fundamental bit period of themodulating sequence, it changes values in such a way to providefrequency content spread across a bandwidth that ranges from twofrequencies that depend upon (a) the repetition period of the modulationsequence (the lowest frequency of interest) and (b) the response time ofthe modulator (the highest frequency of interest). Such a sequence couldbe obtained in practice, for example, by choosing 1's or 0's by the tossof a coin or by a pseudo random number generator.

Likewise, we have found that the techniques according to the presentinvention may apply to different types of propagation paths, includingdispersive analyzers, as well as time-of-flight detectors, or even massspectrometers, dispersive elements, such as prisms in Hadamardspectroscopy, or reflectrons used in TOF mass spectrometery, as well assimple flight tubes used in molecular beam scattering, neutronscattering, and charged particle energy or mass analysis.

A maximum likelihood method may be used as the statistical estimationmethod, using a model of how the object function was converted intonoisier data by the components of the instrument and information aboutthe noise distribution. The noise distribution might, for example, be aGaussian or Normal distribution, or in the case of counting experiments,it might be binomial, which under conditions of rare events, is wellapproximated by a Poisson distribution.

But the statistical estimation technique can itself be one of severaldifferent types of known methods, all of which may be derived fromBayesian probability calculus. Maximum likelihood estimation methods,that optimize the probability of obtaining the data, may be implementedusing algorithms such as the Lucy algorithm (also known as theLucy-Richardson algorithm and the EM algorithm in medical field asdescribed by Shepp and Vardi), and the numerous algorithms described in(Meinel E. S., 1986, “Origins of linear and nonlinear recursiverestoration algorithms”, J. Opt. Soc. Am. A., 3, 787-799.

Other estimation methods maximize the probability of a result, given thedata and other information that is known or assumed about the system,where the result could mean i) a specific model for the system, ii) aspecific number or plurality of numbers, such as the intensity of aspectral line or set of lines, iii) a set of parameters that describethe system, or iv) the underlying object spectrum itself. One class ofestimation methods that maximize the probability of a result is known asBayesian methods, and within this class, those that use an entropyexpression for the probability of the object spectrum are generallyknown as maximum entropy algorithms.

Maximum entropy estimation methods, that optimize the Bayesianprobability of the object, with specific forms of the a prioriprobability of the object (such as a combinatorial probabilityexpression), may also be used to obtain an object distribution from thedata. Algorithms that implement the maximum entropy optimization includeconjugate gradient and other gradient search methods, geneticalgorithms, and Monte Carlo methods.

The algorithm for the optimization method can utilize a convolution or acorrelation; such computational steps can be carried out with any of avariety of methods, including for example Fast Fourier Transforms, PrimeFactor Transforms, serial convolutions, or other techniques. What isimportant is that the statistical recovery method respect theperiodicity of the signal provided to it.

The overall method in the preferred embodiment is preferably aniterative method, although that may not be an absolute necessity inorder to obtain certain advantages.

The modulation function applied to the particle beam may also begeneralized to any substantially random binary sequence of any length.Some prior art applications have used modulation sequences, buttypically these were always PRBS sequences. By lifting this restrictionand that of the cross correlation method, we have determined thatestimation methods, such as the so-called Lucy-method, work withmodulation functions that are not strictly binary. Thus, it should beunderstood that the modulation function also need not be a strictlybinary signal; for example, it might have non-zero or non-100% ofexpected value transmission values. The application of probability basedestimation methods can take into account these attributes of thecomponents of the system and prevent (or minimize) artifacts fromappearing in the result estimated.

Indeed, we have determined that for HREELS-type data, a Lucy-typeestimation method actually works better with substantially random binarysequence that have only about a 20% duty cycle, rather than the moreusual 50% duty cycle restriction associated with maximal lengthsequences. We have, in effect, determined that an instrument may performbetter if the duty cycle of the modulation sequence is actually reducedfrom 50%.

So far, we have found that if one can use a PRBS Maximal Lengthsequence, it will provide better results. However, but if for somereason that is not possible (e.g., signal segment lengths of a power oftwo must be used such as for FFT processing, then there can be anadvantage to reducing the duty cycle.

This also implies that the frequency content of the modulation sequencecan be “designed.” In particular, a truly random sequence would containa broadband encompassing all frequencies from zero to one-half of thefundamental bit period. However, if it is known that a particular partof the spectrum is of interest in advance, i.e., the mass range of aparticular particle is of interest, then sensitivity can be increased inthat frequency range by designing the frequency content of themodulation sequence accordingly.

Depending upon the statistical estimation method selected, the computer150 may also need to obtain an initial estimate. For example, iterativemethods such as maximum likelihood and maximum entropy methods typicallyrequire an initial estimate for the iteration process. This initialestimate can be obtained by a step of determining a single initialtime-of-flight response from the instrument, and storing it. Theestimate might be obtained from cross-correlation with the modulationsequence. The estimate might also be derived from a previous estimateobtained from a set of slowly varying time spectra.

Thus, a mode of operation can be included in the instrument that takessets of modulated data for brief segments of time and then uses thatestimate from a one period as an initial estimate for a subsequent orprevious segments.

It should be understood that the computer 150 may take a number ofdifferent forms, such as a microprocessor, digital signal processor,field programmable gate array, personal computer, array processor, orother processor which is appropriate to the specific intended use of theinstrument. For example, handheld instruments are more likely to useFPGAs whereas laboratory instruments might be quite capable of usingsoftware-programmed personal computer (PCs) or even array processors.

2. Simulation of a PRBS Modulated TOF Instrument Using MaximumLikelihood Method Recovery

Although we have described in this section below a specific simulationthat suggests application of a specific “Lucy” maximum likelihood methodfor a strictly PRBS-type modulated time-of-flight HREELS electronspectrometer, it should be understood that the principles of theinvention can be extended to many other types of spectroscopyinstruments, modulation sequences, and statistical estimation methods.

General principals of the PRBS modulated TOF spectrometer will bediscussed here prior to presentation of our results. A segment of anideal PRBS sequence is shown in the curve of FIG. 3 a, together with itsautocorrelation function in FIG. 3 b. If the modulator, or “chopper” isopen (1) or closed (0) for the duration of a time step, τ, and thesignal over-sampled (here 16×), then the autocorrelation is a triangularpulse with base 2τ. We note that for finite, linear rise and fall times,if the duty cycle is still 50%, as shown in FIG. 3 c, theautocorrelation function (FIG. 3 d) becomes rounded, but approaches zerosmoothly without artifacts.

FIGS. 4 a and 4 b show energy corruption histograms for regionscorresponding to the distance traveled in 0.5 ns time steps for a 2 eVelectron beam for an interleaved comb chopper with wire spacing of 1.2mm and radius 25 μm with applied voltages of ±0.4 V and an analyzeracceptance angle of ±1°. From these results, it is clear that the energycorruption is large compared to the energy resolution of modem HREELSmonochromators for the first 2-3 ns before or after switching thepotentials. Thus, reducing the time base of the PRBS sequence belowabout 8 ns (for this chopper) significantly degrades the effectiveresolution of the analyzer for the affected electrons. However, energycorruption is introduced only at the beginning and end of multiple 1'sin the PRBS modulation sequence, i.e. when the chopper voltage ischanged. There are 2^(n−1) edges in a sequence of length 2^(n)−1, so thefraction of electrons which pass the chopper un-corrupted depends uponthe time base, τ, but is about 50% less than would be the case for asingle pulse TOF experiment.

We then prepared a computer simulation of an instrument that defines thechopper response function, p, as the effect of the time-dependentchopper potentials on the detected electron beam current. Moreprecisely, p is the chopper transmission function,p(t)=I _(det)(t)/I _(o)(t).For the data described here, p is given by a 255 bit (2⁸−1) PRBS(“maximal length shift register”) sequence, with either an ideal stepfunction response, or including one or more of the artifacts describedbelow. Data are typically generated with the PRBS time base of τ=8 ns,and oversampled by factors of 8, 16 or 64, corresponding to detectortime bins of 1 ns, 0.5 ns, and 0.125 ns, respectively. A typical HREELSloss spectrum was simulated with a Gaussian elastic peak (E_(p)=2 eV;full width at half maximum (FWHM)=2 meV; 100 kCts/s) and a set ofsmaller Lorentzian peaks of relative intensity 0.1-10% and FWHM 3 meV,representing inelastic gains and losses. Several doublets, whoseseparation was greater than or equal to their FWHM, were included totest the resolution enhancement capabilities.

Previous experience with the Bayesian/maximum likelihood algorithmsindicates that two peaks must be separated by at least their FWHM to beresolvable.

One asymmetric lineshape and a feature in the tail of the elastic peakwere also included to test the ability of the deconvolution algorithmsto distinguish overlapping peaks from asymmetric ones.

To generate PRBS modulated, time-series data, (p{circle around (x)}o),the following procedure was followed. The kinetic energy distribution(energy loss spectrum) was converted into a TOF distribution, o=N(t),with an integer number of counts in each of the discrete flight timebins (note that the object function is defined in the time domain, notthe energy domain). A probability function, ƒ(t′), was generated bycreating a cumulative sum of counts over the array of flight times,

f(t^(′)) = ∫₀^(t^(′))N(t) 𝕕t.The probability function, ƒ(t′), represents a look-up table, such that arandom number chosen over the domain of ƒ implies a flight time, t. Toinclude energy corruption effects, each of the eight energy corruptionhistograms, corresponding to the seven energy corrupted spatial regionsof FIGS. 4 a and 4 b and the uncorrupted distribution, were convolutedwith the original energy distribution before being converted to TOFdistributions.

For a beam current of 10⁵ Cts/s, the probability of detecting oneelectron per PRBS cycle is, from the simulation,(10⁵ s ⁻¹)(255 bins/cycle×8 ns/bin)=0.2/cycle.This shows that the noise should obey Poisson statistics and indicatesthat accumulation over millions of PRBS cycles is required forsufficient signal to noise to recover the object spectrum. Simulateddata was produced by cycling through the response function, p, where oneach time step, i, p(i) ranged between 0 and 1. If p(i) was greater thana random number between 0 and 1, the gate was “open” and the electron'sflight time, t′, was chosen randomly from ƒ(t′). (Because theprobability of selecting any one flight time from the distribution issmall, a plot of the variance of the number of counts in each channel,for a single pulse TOF spectrum, was equal to the average count rate ineach channel, demonstrating that a Poisson noise distribution wasobeyed.) Then to generate PRBS modulated data, a count was added to thechannel corresponding to the flight time plus the position in themodulation sequence, p. If the channel number exceeded the length of thesequence, the value was wrapped around by the PRBS sequence length (8,16 or 64 times 255). The process was continued, cycling through p untilthe desired number of total counts was recorded, producing data setswith 2 million to 256 million counts (MCts).

3. Simulation Results of Cross Correlation Vs. Maximum LikelihoodRecovery

We also compared the results of the standard cross correlation methodwith our maximum likelihood deconvolution. The cross correlation methodsuch as that described in Skold, K. et al., Instrum. Methods 63 (1968)pp. 114-116 was performed in MatLab (MathWorks, Inc., 5^(th) ed.,Natick, Mass.), resulting in a recovered spectrum, r=(p⊕p){circle around(x)}o. A maximum likelihood estimate of the object function, o, wasobtained from the modulated data, (p{circle around (x)}o), using thewell known, iterative LUCY algorithm referenced and describedpreviously. The Lucy algorithm maximizes the probability, P(y|o), ofobtaining the data, y=o{circle around (x)}p, given an object function,o, for a Poisson noise distribution:

${P\left( {y_{i}\text{❘}o} \right)} = \frac{\left( {o \otimes p} \right)_{i}^{y_{i}}\exp\left\{ {- \left( {o \otimes p} \right)_{i}} \right\}}{y_{i}!}$by an iterative process in which the estimate at o^(k) is used togenerate the next estimate, o^(k+1).

$o_{i}^{k + 1} = {{o_{i}^{k}\left( \frac{y}{\left( {o \otimes p} \right)} \right)} \oplus {p.}}$

For deconvolution of the PRBS modulated data, the initial guess wasobtained from the result of the cross correlation method, r, and a primefactor transform was utilized instead of the usual FFT algorithms, sincethe sequence length is not a power of 2. Because the PRBS modulated datais periodic (edge effects associated with the start of data acquisitionare or can be made negligible), no packing of the array is required.

Consider first the effects of the Poisson noise distribution on thecross correlation method. FIG. 5 compares the object spectrum with thespectra from a single pulse TOF experiment (broadened by the 8 ns gatetime) and that recovered from a PRBS modulated experiment. The PRBSmodulated data was generated using the ideal, step function sequence forp and the object function, o, shown. The single pulse TOF spectrum,generated with a square gate function of 8 ns duration, results indegraded resolution and poor signal/noise compared to the PRBS recovereddata. Despite the fact that the cross correlation method is not strictlyvalid due to the Poisson noise distribution, the method works reasonablywell, presumably since the Poisson noise distribution approaches anormal distribution for sufficiently large count rates.

FIG. 6 compares the results of maximum likelihood recovery with thecross-correlation method. In this example, the modulation function, p,contained linear rise times of 1 ns (c.f. FIG. 5) but maintained a 50%duty cycle. Results given in FIG. 6 are for 256 MCts in the modulateddata. The resolution of the cross-correlated spectrum is degraded, asexpected, by convolution with an approximately triangularautocorrelation function (c.f. FIG. 3 d). In the maximum likelihoodrecovery method, the results improve with both the total number ofcounts in the data and the number of iterations. As iterations proceed,the Lucy algorithm refines the spectral estimate, significantlyimproving spectral resolution, while artifacts remain at a level of lessthan 0.01%. Note that the gain peaks at 1160 ns, corresponding to 18 meVseparation, are clearly resolved.

Finally, we determined the results of maximum likelihood recovery whenthe modulation function, p, includes both energy corruption effects andthe time lags predicted by the model potential for an interleaved combwith applied voltage, V_(app)=±0.4 V, wire spacing, d=1.2 mm, radius,R=25 μm and an acceptance angle of ±1°(see below for details of thisdesign). The modulation function p, used for deconvolution, wasgenerated in the same way as the data, except that it included only theelastic peak energy distribution. Thus, p characterizes both themonochromator energy distribution and the non-idealities of the chopper,and would be measured in practice simply by directing the monochromaticbeam directly into the TOF detector.

FIG. 7 a compares the true object spectrum with results of the Lucyalgorithm as a function of the number of iterations. This data wasgenerated with a detector time bin of 0.5 ns, and the reproduction ofthe object function is excellent. Note that the feature in the base ofthe elastic peak is well resolved and the resolution of the doublets arecomparable to that in the true object spectrum (the doublet at 1230 nscorresponds to 9 meV separation). Even the feature at 1340 ns withintensity 0.1% of the elastic peak is recovered with an intensityroughly an order of magnitude greater than the noise. Thus, the Lucyalgorithm is able to account for both the rather substantial energycorruption effects of the chopper and the Poisson noise distribution.

By contrast, FIG. 7 b shows the cross correlation results using theideal PRBS response function to process the same energy-corrupted, PRBSmodulated data. The negative artifacts appearing between channels 1700and 1900 ns are consistent with the non-ideal behavior of the chopper,leading to an autocorrelation function similar to that shown in FIG. 5f. To illustrate the importance of accurately defining the modulationfunction, we also show the results of the maximum likelihooddeconvolution using the ideal PRBS (c.f. FIG. 3 a) sequence. Artifactsappear at the positions of the negative artifacts in the crosscorrelation recovery and the true features are split.

We have thus shown for the first time that maximum likelihood methodscan be combined with PRBS modulation to achieve resolution enhancement,while properly accounting for the Poisson noise distribution andartifacts introduced by the chopper. The results suggest that resolutionsimilar to that of modem high resolution electron spectrometers can beachieved with a dramatic performance (throughput) advantage overconventional, serial detection analyzers.

4. Modeling an Interleaved Comb Chopper

In our work, we have fabricated choppers using two different methods.The first design utilized a circular, laser-cut ceramic disc with twosets of holes spaced 0.3 mm apart. Tantalum wire (50 μm dia.) was handwired to achieve 0.6 mm or 1.5 mm spacing between oppositely chargedwires. The two distinct wire sets are electrically isolated from oneanother by the ceramic plate and terminated on each line with a pair ofsurface mount 100 Ω resistors in parallel. A second type of chopperfabrication used lithographic methods. Gold 50 ohm microstrip leads werepatterned onto polished square alumina substrates using the lift-offmethod. Gold wires were then positioned using a jig to align and tensionthe wires, which were bonded using a parallel gap welder (UNITEKequipment, UNI Bond (II), Model (50F)). With this method, wire diametersof 25, 50 and 100 μm, centered on inter-wire spacings of 250, 500 and1000 μm, respectively were achieved. The set of three chopper types, allwith 90% transmission, were designed to test the dependence of opticalproperties on the scale of the device.

We have considered that if the chopper is open or closed for theduration of a time step, τ, and the signal is over-sampled (e.g., 16×),then the autocorrelation is a triangular pulse with base 2τ. We havealso determined that for finite, linear rise and fall times, if the dutycycle is still 50%, as shown in curve c, the autocorrelation function(curve d) becomes rounded, but approaches zero smoothly withoutartifacts.

The problems that arise in applying such a charged particle gate, knownas an “interleaved comb” or Bradbury-Nielsen gate, to PRBS modulated TOFmass spectrometry have been recognized by Brock, et. al in Rev. Sci.Instrum. 71 (2000) pp. 1306-1318. We examine here in detail theartifacts that are introduced for electron spectroscopy, and that ourstatistical estimation method can be used to undo their effects. Threeeffects can be distinguished:

i) The “dead time” associated with the time for electrons to cross thefield affected region leads to an error in the time at which the beamturns on and off. When the PRBS sequence differs from a 50% duty cycle,the autocorrelation function contains oscillations in the baseline andnegative artifacts. This effect is analogous to the effects of machiningerrors and the finite thickness of mechanical chopper disks, used forexample in molecular beam scattering. These negative artifacts can beassessed from the autocorrelation of the (imperfect) PRBS sequence, andremoved a posteriori, although the effects of the finite disk thicknesslead to a velocity dependent error.

ii) The interaction of charged particles with an electrostatic gatecauses a change in the energy of the particles when the potentials areswitched. The change in energy, which we term “energy corruption”, leadsto degradation of the information carried by the charged particle, i.e.its energy or velocity, which is the quantity measured in TOFspectrometry. Because the corruption depends upon the position of theelectron, relative to the plane of the chopper, at the time thepotential is switched on or off, a statistical distribution of energycorruption can be determined directly from the potential for spatialregions as a function of the distance from the chopper.

FIGS. 8 a and 8 b show a schematic diagram of the system designed tocharacterize the chopper response, based upon an HREELS spectrometer(FIG. 8 a) (McAllister Technical Services, Model PS200, Coeur d'Alene,Id.) and a custom designed time-of-flight detector (FIG. 8 b). Thechopper was mounted at the center of rotation on the face of themonochromator and rotated with it. The electron beam was focused by themonochromator lens through the chopper into the analyzer to characterizethe energy (typically 10-20 meV FWHM) and angular (typically ±0.7°)distributions of the incident electron beam. When static potentials wereapplied to the chopper, the monochromator was rotated by a stepper motorunder computer control to measure the angular distribution of thedeflected beams. Data acquisition and control were performed using aSPECTRA card (Ron Unwin, Cheshire, UK) customized with a user-writtendynamic link library (DLL). When the monochromator was rotated so as todirect the beam into the TOF detector, a modulated signal was applied tothe chopper grid and the time-dependent response was measured. The TOFdetector was based upon a micro-channel plate detector (AP-TOF, GallileoCorp., Sturbridge, Mass.) which was custom-modified for negativeparticle detection.

We first present a two-dimensional analytical potential, based upon aconformal mapping of an infinite, periodic set of infinitely long, linecharges ±λ, onto two line charges, as illustrated in FIGS. 9 a and 9 b.In real space, wires of diameter 2R and alternating potential, ±V_(app),are spaced along the y-axis with a periodicity, d. Electrons passing inthe positive x direction would be deflected in the ±y directions. Inreal space, let α=x+iy. Using the complex transformation,

${\eta = {\exp\left( {\frac{\pi}{d}\alpha} \right)}},$the line charges alternately map onto the points (0, ±i) in the η spacefor which the potential is well known. The infinite chopper potential isthen

${\psi\left( {x,y} \right)} = {\frac{\lambda}{2\pi\; ɛ_{0}}{{\ln\left\lbrack \frac{{\cos\;{h\left( \frac{\pi\; x}{d} \right)}} + {\sin\left( \frac{\pi\; y}{d} \right)}}{{\cos\;{h\left( \frac{\pi\; x}{d} \right)}} - {\sin\left( \frac{\pi\; y}{d} \right)}} \right\rbrack}.}}$The contours of this potential in the real space are nearly circularclose to the line charges. Thus, for finite diameter wires with R<<d,i.e. near unity transmission, the line charge solution well approximatesthe actual chopper potential and we need only choose a point throughwhich the potential passes to define the line charge λ. Choosing thepoint (x=0, y=d/2−R) to have a potential V_(app) we have

$\lambda = \frac{2\pi\; ɛ_{0}V_{app}}{\ln\left\lbrack \frac{1 + {\cos\left( \frac{\pi\; R}{d} \right)}}{1 - {\cos\left( \frac{\pi\; R}{d} \right)}} \right\rbrack}$The potential (see FIG. 13 below) is simply proportional to V_(app), andhas the important feature that it decays as

${{\left. \psi \right.\sim 4}\lambda\;{\exp\left( {- \frac{\pi{x}}{d}} \right)}},$such that the potential is reduced to <3% of eV_(app) within the first dspacing, and is <10⁻⁵ eV_(app) within 3.6 d. For V_(app) of order 1V,the potential for x>3.6 d is small compared to the typical (˜2 meV)energy resolution of the monochromatic beam in HREELS.

Trajectory calculations were performed numerically using an adaptive,4^(th) order Runge-Kutta method (MathCad v. 6 and 2000, Mathsoft) withinitial positions chosen randomly in a region of negligible potential tothe left (x<0) of the chopper plane. The distribution of angulardeflection was determined from the final angle after the electron leavesthe field affected region (ψ<10⁻⁵ V_(app)). To simulate time-dependentchanges in chopper potential, trajectories were calculated either infree space or over the applied potential, assuming that the potentialwas changed instantaneously, until the electron was in a region ofnegligible potential. For comparison with experimental data, flighttimes were calculated from the final position and velocity to thedetector at a chopper-to-detector distance of 16 cm.

In FIG. 10 a, we compare the angular distribution of the deflectedelectrons as a function of the applied potential using the TOF detector.Similar results were obtained with the dispersive analyzer. For smalldeflection angles and a beam size large compared to the wire spacing, d,the beam is split into a symmetric distribution, peaked at angles±θ_(def), which is approximately linear in the ratio of the appliedpotential to the electron's kinetic energy, eV_(app)/KE. The modulationof the beam is clearly dependent upon both the angular distribution ofthe incident electron beam and the deflection angle. For an acceptanceangle of ±1°, 99.9% modulation is easily achieved under static DCapplied potentials. As expected, the deflection angle is independent ofthe wire d spacing, for constant transmission, or R/d; i.e., as thechopper geometry is scaled to smaller dimensions, the applied voltagemust remain constant to achieve the same deflection angle.

The time-dependent response, illustrated in FIGS. 11 a-11 c for the2R=100 μm chopper, was measured for a range of applied voltages with anincident 5 eV beam having an angular distribution of FWHM=1.5°. Thepotentials were periodically dropped to zero for approximately 100 ns(period of 700 ns; rise/fall times of 1.5 ns), during which time theundeflected beam was accepted through an aperture of half acceptanceangle θ_(a)=1.5°. The number of counts in each histogram varied withacquisition time, but are shown as counts to allow the noise level to becompared with that expected from the Poisson distribution. The TOFhistograms, sampled on 250 ps time bins, show that rise times of <0.5 nsare easily achieved. However, several features of the TOF spectra shouldbe noted. First, the histograms display peaks and tails at the timeswhen the chopper changes state that are significant compared to thePoisson noise distribution. Second, a detailed comparison of the timethat the potentials are off and the time that the beam is on shows thatthe difference varies with applied voltage: the electron beam turns onlate and/or shuts off early. The dependence is shown in FIGS. 11 a-11 c.Third, while the background on the high energy side (shorter flighttimes) is less than 10⁻⁵ of the average count rate when the gate isopen, significant intensity with a distribution to lower energy appearswith a relative intensity of 10⁻², which is attributed to inelasticscattering from the relatively thick apertures placed before and afterthe chopper. The origin of the first two features is discussed in lightof the theoretical simulations presented in the following section.

5. Theoretical Simulation Results and Discussion

We first compare the angular distribution of the transmitted beam (FIG.10 a) with simulations based upon trajectory calculations. Electronpositions were chosen randomly within the region (−40 mm<x<40 mm, −1.8mm<y<1.8 mm). The potentials were applied for the first 8 ns, duringwhich time the first electrons enter the field-affected region. Thepotentials were then turned off for 8 ns, on again for 8 ns, and finallyturned off to calculate the final angle and the time in the field-freeflight tube to reach the detector. FIG. 10 b shows the angulardistributions calculated for the infinite chopper corresponding to thegeometry of the ceramic disk design as a function of V_(app). Theresults show that, for uniform filling of the chopper, the deflectionangle in the angular distribution increases proportional to V_(app), andas shown superimposed in the inset of FIG. 10 b, agrees quantitativelywith the experimental measurements. Whereas the electrons in thesimulation have initial velocity parallel to the x-axis, the angulardistribution in the experiment leads to broader peaks in the angulardistributions of FIG. 10 a.

The results of simulations of the time-dependent response are shown inFIGS. 12 a-12 f. The flight times, for electrons accepted by an apertureof ±1° at a flight distance of 160 mm, are shown as a function ofV_(app) in FIGS. 12 a, 12 b and 12 c, and as a function of the wirespacing (for constant R/d) in FIGS. 12 d, 12 e, and 12 f. While the beamis modulated as expected, turning the beam on then off and on again,several features noted in the experimental data are reproduced in thesimulations. First, at the transitions, the simulations predict spikesand tails in the histograms which deviate significantly from the Poissonnoise distribution. Second, the chopper response (here, thetime-dependent beam current) has a lag or lead with respect to theapplied voltage making it appear that the gate is open for times lessthan the 8 ns used in the calculation and closed for times greater than8 ns. These effects depend upon V_(app) as well as the scale of thechopper (i.e. the wire spacing for constant R/d). Reduction of theapplied voltage or reduction of the wire spacing noticeably decreasesthese effects. We note that late opening and early closing of the gateis analogous to the effect of finite thickness investigated byZeppenfeld, et al., in Rev. Sci. Instrum. 64 (1993) 1520-1523 forspinning disk mechanical choppers.

The origin of the spikes and tails can be understood from considerationof trajectories over the potential, shown in FIG. 13. Consider first anelectron approaching the chopper with potentials off (gate open). If anelectron is near the gate and the potential is applied instantaneously,the electron may gain or lose potential energy, depending upon whetherit is closer to a negative or positive wire, respectively. For electronsin the field affected region, it is necessary to compute trajectories todetermine whether sufficient transverse field exists over the subsequentpath to exclude the electron from the detector. Clearly, this isdependent upon the applied voltage and the acceptance angle of theaperture. The curves in FIG. 13 represent critical boundaries,ƒ(x,y,θ_(acc),V_(app)), such that if the electron is beyond the curve atthe time the potentials are applied, the electron has final angleθ_(ƒ)<θ_(acc); i.e. the electron reaches the detector. For thoseelectrons which reach the detector, their energy has been changed, orcorrupted, by an amount given by the potential at the position of theelectron when the potential was changed. Thus, there is a distributionof “energy corruption”, ranging from −V_(app) to V_(app). The symmetryof the potential shows that, for a uniformly distributed beam ofelectrons, the probability of gaining energy is equal to that of losingenergy, producing both longer and shorter flight times in the TOFhistogram. Therefore, the step function response of the chopper ismodified, producing the spikes and tails, which we attribute essentiallyto these energy corruption effects. As V_(app) is decreased, the maximumenergy corruption decreases, and the magnitude of the spikes and tailsin the simulations decrease.

The energy corruption effect can be distinguished from a second effect,noted in both the experimental and simulation data, that there may be alead or lag in the beam current with respect to the times that thepotentials are changed. Consider an electron approaching the gate, withV_(app)=0, along the mirror symmetry plane, x=0, such that ψ(x=0, y)=0.For this trajectory, the energy corruption effect is zero, since theelectron is still at zero potential immediately after the voltages areapplied. It propagates over some trajectory until leaving the fieldaffected region, where again the potential is zero. However, dependingupon the acceptance angle of the detector, the electron may or may notbe detected. If the acceptance angle is small, e.g.(KE/V_(app))θ_(acc)=2.5°, only electrons that have passed the plane ofthe chopper (by approx. ½ d in this example), reach the detector: thechopper appears to close early. By contrast, for large acceptanceangles, electrons in a small region before the gate (e.g. y≧−0.3 d for(KE/V_(app))θ_(acc)=20°), still reach the detector and the chopperappears to close late.

Examination of the boundaries for several combinations of V_(app) andθ_(acc) reveal that, since the deflection is approximately linear in theapplied voltage for small deflection angles, the boundaries are afunction of the ratio θ_(acc)(KE/V_(app)). Likewise, a scaling argumentreveals that, for constant R/d ratio, the trajectories are the same ifthe electron enters from the field free-region independent of wirespacing, d, as long as the kinetic energy and applied voltage areconstant. This implies that the deflection angle is only a function ofthe ratio eV_(app)/KE for the static chopper potential. Therefore, theinformation about the boundaries in FIG. 13 summarizes everything thatcan be known about the time dependant optical properties of the chopper,within the approximation of a single, instantaneous change in chopperpotential. Examination of trajectories corresponding to the reverseprocess, namely electrons approaching the gate with the potentialapplied and then turned off while in the neighborhood of the chopper,shows that the boundaries are essentially just the mirror image aboutthe plane of the wires and the effects on the time lead or lag mirrorthe previous case: if for a small acceptance angle the beam turns offearly, then it turns on late.

Since the energy corruption depends simply on the position of theelectron when the potential is switched, the distribution of energycorruption for a given region of space near the chopper can be extractedsimply as a histogram of the potential values (for electrons in theregion which reach the detector). As discussed above, we utilize thispotential to simulate the effects of energy corruption and lead/lag inbeam response on TOF spectra for applications in HREELS.

Thus we have shown that for near unity transmission, trajectorycalculations on the potential derived from a conformal map agree wellwith experimental measurements characterizing both the static deflectionand time-dependent response of the chopper, suggesting that thispotential is a useful limiting-case description of the interleaved combdevice. The finite penetration of the field beyond the plane of thechopper leads to non-ideal chopper response, which is characterized interms of an energy corruption effect and lead or lag in the time atwhich the beam responds to the chopper potential.

6. Other Embodiments of the Invention

Alluded to above was the fact that the invention can be applied to othertypes of spectrometers. One preferred embodiment of the invention formass spectrometry, for example, is shown schematically in FIG. 14.

In such an instrument, ions must first be created in a source, forexample by electron impact, chemical ionization, or electrosprayionization. In electron impact, electrons emitted from a heated filament300, held at a potential −V_(fil), are accelerated into a cage 304 heldat +V_(ion), where ionization of molecules occurs. Ions are thenextracted from the cage 304 by a grid 308 of less positive potential,V_(xtr), and then collimated to produce a narrow angular distribution bythe collimator slits 310. The beam of ions is either deflected by the“chopper” or modulator 315 (beam “off”) or passes undeflected (beam“on”), depending upon the state of the applied voltage at the time theion approaches the gate. A third collimation slit 318 selects theundeflected ions.

We now turn to optimal operation of the ion gate. We have shown (FIG.13) that for a given ion energy, there is a critical boundary,characterized by the ratio (KE/V_(app))θ_(acc), beyond which ions musthave passed at the time the potential is turned off for the ion to stillreach the detector 320. For a monochromatic ion energy distribution, allions follow the same trajectory, although at different speeds due totheir range of masses. When the critical boundary lies after the planeof the modulator or “chopper” grid 315, the response of the beam to theapplied voltage appears to turn off late and turn on early. The lead orlag time is dependent on the particle's velocity, and for a distributionof ions with different masses but the same energy, the lead or lag ismass dependent. However, by choosing the ratio of K=(KE/V_(app))θ_(acc),so as to place the critical boundary on the plane of the chopper, thelead/lag effects can be minimized. The value illustrated in FIG. 13corresponds to a radius to spacing ratio of 10, or a nominal gridtransmission of 90%. The values of K are easily determined fromtrajectory calculations for other geometries of the Bradbury-Nielsengate. To the extent that the extracted ions have a finite energydistribution, the critical boundary is blurred, and the rise and falltimes associated with the response of the beam to the chopper 315 willbe degraded.

Although the lead or lag can be optimized, the energy corruption effectsmust be handled in a separate manner. Depending upon the position of theion at the time the potentials on the gate are switched, the ions willgain or lose energy. However, because the acceptance angle of thecollimated beam can be chosen to be small (typically ≦1°), the energyspread is essentially confined to the x-direction. For mass spectrometryapplications, it is therefore possible to perform an orthogonalacceleration into a drift tube or a reflectron 330 (FIG. 14). With thesimple drift tube geometry, the energy spread in the orthogonaldirection, E_(y+), leads to broadening of the TOF spectrum. However, theenergy distribution is, to a good approximation, the same for all ions,regardless of mass, and can be accounted for in the statistical recoverymodel. In the case of a reflectron, the effects of the energy spread arefurther reduced due to the compensation in arrival time for a properlydesigned flight tube and ion mirror geometry. The second orderaberrations of the reflectron can be corrected with a quadraticpotential gradient, which is more complicated to implement in theinstrumental hardware. Our method accounts for these aberrations in thestatistical model.

FIG. 15 illustrates schematically an algorithm to model the massspectrum. The possible masses of ions are determined by the isotopemasses, and hence can be properly described by a discrete set of deltafunctions. The possible mass values are fixed; only the intensities arevariable. To the extent the ions have the same angular distributionafter the collimator, their spatial distribution in the orthogonalacceleration region is similar and the distribution of energies afteracceleration to the drift velocity is similar. In the simple drift tubeconfiguration, the spread in the arrival time is mass dependent.However, the energy distribution can be estimated from a calibrationstandard, such as high purity helium gas. The model therefore consistsof calculating the single pulse TOF spectrum based upon the intensitiesof all possible mass peaks and the common ion energy distribution (moreprecisely, the E_(y) component), followed by convolution with themodulation sequence. The optimization therefore consists of adjustingthe magnitude of the mass peaks, assuming the shape of the ion energydistribution and the chopper response function have been obtained inprevious instrumental calibration steps.

Although the simulation results presented above were for strictlymaximal length pseudo random bit sequences (ML-PRBS), the invention hasapplication to other modulation sequences as well. FIGS. 16A and 16Bcompare the results of such ML sequences with more random, non-MLsequences.

Object spectra from a TOF instrument were first simulated as shown inthe plot of FIG. 17 a. The spectra typically consists of several peaksranging in height down to 0.1% of the largest peak (please note that forthe sake of clarity, only the “bottom 1%” of the plot is shown;otherwise the large dynamic range of the dominant spectral line wouldobscure the artifacts in the plot. An example raw object spectrum isshown in the lowest trace.

We then applied a non-ML, purely random sequence of bits with a 25% dutycycle as a modulation function for 1000 iterations. By duty cycle, werefer to the fraction of “0”s in a sequence of “0”s and “1”s in themodulation sequence. The spectrum of FIG. 17 b then resulted. Note thatthe line spectra occur in the expected places with little or noartifacts being added.

Indeed in another example, shown in the plot of FIG. 17 c was the resultfor a 50% duty cycle. Note the artifact that has been introduced atabout 1600 ns, which could result in errors in the interpretation of theresults. Apparently then, reducing the duty cycle from 50% can provideadvantages.

FIGS. 16 a and 16 b analyze the results of modulating with fourdifferent modulation sequences as a function of the number of iterationsusing the Lucy algorithm. These included:

-   -   a 9 bit maximal length shift register (ML) sequence (length=511        bits)    -   an 8 bit maximal length shift register sequence (length=255        bits) with three additional zeros inserted at each zero in the        sequence to produce a 25% duty cycle sequence with the same        length as the 9 bit sequence    -   a random sequence with a 50% duty cycle; and    -   a random sequence with a 25% duty cycle

All sequences were over sampled by expanding each bit 8 times to producea 4088 bit sequence.

To simulate the instrument, the object spectrum was then convoluted withthe modulation sequence using an FFT; Poisson noise was also added todata at this point. The spectrum was then recovered from this data usingthe Lucy algorithm, with various numbers of iterations.

For each recovered spectrum, the largest artifact was determined byexamining the parts of the spectrum where there were no known peaks; asignal to noise ratio (S/N) was then calculated for the 0.1% and 1%peaks using the largest artifact as the noise reference level.

The plots show the standard ML sequences gives the highest S/N for agiven number of iterations. The plots also show that S/N for a givenpeak, but different sequences, converges to similar values for largenumber of iterations. These results illustrate that if a standard MLsequence is not possible or desirable, a non ML sequence can be usedwithout sacrificing S/N. Since ML sequences are well known, non MLsequences may be more desirable for encryption applications. Asprocessing speeds continue to increase, large numbers of iterationbecome less of a problem. Since the first iteration is the crosscorrelation of the data with the sequence, the plot shows that Lucyimproves the S/N over the cross correlation.

In summary, we conclude that probability based estimation methodsrecover the most probable result, given the limited informationavailable. The information content is determined by many factors,including the signal to noise ratio, sampling rate, sequence length,sequence frequency content (compare for example the difference betweenthe use of maximal length sequences and the sequence of randomly chosen1's and 0's), as well as the bandwidth of the system response functionand underlying TOF spectrum. Note that if the underlying TOF spectrumhas broad, slowly varying peaks compared to the system responsefunction, then the instrument has sufficient resolving power that thereis nothing to be gained by deconvolution as far as resolution isconcerned, although artifacts may still be produced if the recovery isperformed with the cross-correlation method. Conversely, if the TOFspectrum has peak widths that are comparable to or less than the singlepulse response function of the system, then the instrument limits theresolution and resolution enhancement is possible.

While this invention has been particularly shown and described withreferences to preferred embodiments thereof, it will be understood bythose skilled in the art that various changes in form and details may bemade therein without departing from the scope of the inventionencompassed by the appended claims.

1. An apparatus for analyzing particles comprising: a particle beamsource providing a beam of particles; a modulator for modulating theparticle beam by passing it substantially unaltered during on periods,and affecting the beam during off periods according to a broadbandbinary sequence to produce a modulated particle beam; a detector fordetecting at least one attribute of the modulated particle beam andgenerating a detector output signal in response thereto; and a processorfor recovering information from the detector output signal using aprobability based estimation method, the probability based estimationmethod: i. compensating for non-ideal characteristics of the componentsof the apparatus by using a system response function, p, that representsa response of the particle beam to the broadband binary sequence; andii. allowing higher resolution to be obtained than a nominal time unitcorresponding to a clock period of the modulator would provide withoutthe broadband binary sequence, depending upon at least one of a signalto noise ratio, an oversampling of the time base, and a rise time of theresponse function.
 2. An apparatus as in claim 1 wherein a maximumlikelihood method is used as the probability based estimation method. 3.An apparatus as in claim 1 wherein a Bayesian method is used as theprobability based estimation method.
 4. An apparatus as in claim 1wherein the system response function, p, is obtained from a model of atleast one component of the apparatus selected from a group consisting ofthe beam source and the detector.
 5. An apparatus as in claim 4 whereinthe probability based estimation method characterizes noise in the atleast one selected component.
 6. An apparatus as in claim 5 wherein thenoise characterization is selected from a group consisting of Gaussianand Poisson.
 7. An apparatus as in claim 1 wherein the informationrecovery processor is a software program running in a digital signalprocessor.
 8. An apparatus as in claim 1 wherein the results of theinformation recovery processor are histogrammed.
 9. An apparatus as inclaim 1 wherein the particle beam is selected from a group consisting ofions, electrons, neutrons, molecules, and photons.
 10. An apparatus asin claim 1 wherein the probability based estimation method is aniterative method.
 11. An apparatus as in claim 1 wherein the broadbandbinary sequence is a Pseudo Random Binary Sequence (PRBS).